Workshops Description

WhiteboxTools for Geomorphometry and LiDAR Data Processing

Convener(s): John LindsayProf. John Lindsay explores applications of geomorphometry to spatial hydrology and geomorphology. He is particularly interested in applications involving airborne laser scanning and terrestrial LiDAR. The use of DEMs to model surface drainage patterns, including the handling of topographic depressions in flow-path modeling, has been his career-long research theme. His research projects often involve the development and testing of novel techniques for spatial analysis, for which he has developed open-source GIS software to serve as platforms for these research contributions. He is the lead developer of the WhiteboxTools. He delivered workshops for government employees and conservation managers at the Ontario Ministry of Agriculture and Rural Affairs (OMAFRA) and the Ontario Ministry of Natural Resources and Forestry (OMNRF) and various Canadian conservation authorities. Additionally, he frequently teaches using the WhiteboxTools software, including both undergraduate and graduate level GIS/Remote Sensing courses.

Abstract:

WhiteboxTools is an advanced geospatial data analysis platform that interfaces with common GIS software, including QGIS and ArcGIS, as well as providing geoprocessing capabilities for Python and R scripting. WhiteboxTools possesses numerous capabilities for GIS and remote sensing data processing but also offers particular strength for geomorphometry, LiDAR data analysis, and spatial hydrology. This proposed workshop will introduce attendees to topics including:

Introducing the WhiteboxTools project

Software installation

Interfacing with Python scripting and the QGIS front-ends

Using Whitebox to create DEMs from LiDAR point clouds (ground point separation and interpolation)

DEM pre-processing (DEM smoothing, depression removal)

Multiscale topographic attribute characterization

Flow-path modelling

Attendees will be walked through the process of installing WhiteboxTools and accessing processing tools from both the Python and QGIS interfaces.

External links:

The WhiteboxTools user manual is available here:

https://jblindsay.github.io/wbt_book/intro.html Which includes several tutorials. Additionally, I have prepared a large number of lab assignments for undergraduate courses that use WhiteboxTools, which are available online.

For example, this lab uses WhiteboxTools for Lidar analysis: https://jblindsay.github.io/Courses/F19/GEOG2420/Lab4/ And this introductory lab walks participants through the set-up process https://jblindsay.github.io/Courses/W19/GEOG3420_Lab1/part2.html

Requirements:

Attendees should have Python 3 and QGIS previously installed on computers used in the workshop. The computers used in this workshop can be running any of MS Windows, Linux, or MacOS and should have a minimum of 8 GB of memory and a modern, multi-core processor. Attendees can either use their own laptops or the lab’s computers for this workshop.

Prof. Zbigniew Zwoliński has been dealing with the issues of geomorphometry and geodiversity since the end of the 20th century. He is the chair of the International Association of Geomorphologists Working Group on Landform Assessment for Geodiversity, operating since 2013. Zwoliński have been teaching the course "Geodiversity and geoheritage in Europe" at their home university for several years.The team presenting the course also ran an international workshop “Geomorphometry and Geomorphological Mapping” during the International Association of Geomorphologists Tectonic Geomorphology Summer School on “Alps vs Apennines: Tectonic Geomorphology of Mountains” in 2016, and the International Association of Geomorphologists Working Group on Landform Assessment for Geodiversity Workshop on "Geodiversity in high mountain areas".

Abstract:

The natural environment consists of a variety of correlated biotic and abiotic systems, responsible for diversity in nature. Geodiversity is known as the variety of different abiotic elements (geological features, soils, landforms, hydrographic objects, even climatological conditions) of a given area or region. Among them, landforms play an extremely important role, which are mainly responsible for the physiognomic appearance of the landscape. Landforms occur in nature at different spatial and temporal scales. Moreover, the origin and development of landforms are extremely diverse.There are numerous methods for geodiversity mapping and assessment. These methods may be classified based on two criteria: (i) source of the data, which may be direct or indirectm and (ii) procedure, which may be qualitative, quantitative and qualitative-quantitative (Zwoliński et al. 2018). Indirect geomorphometric features seem to be the best, objective indicators of various morphological landscapes.The aim of the workshop is to present the indirect method of geodiversity assessment based on geomorphometric surface parameters (morphometric, hydrological, climatic etc.) and objects (landforms etc.) derived from the digital elevation model of any area. Two variants of geoinformation analysis workflow will be presented. The first is associated with map algebra, the second with analytic hierarchy process. The final maps of geodiversity will be verified using landscape metrics.

Installed software is required for the workshop: ArcGIS ver. 10.X, ArcGIS Pro ver. 10.X, SAGA GIS 7 and Fragstats ver. 4.2. Attendees should mainly use their own laptops for this workshop. SAGA will be available in the lab’s computers. A limited number of workstations equipped with ArcGIS will be available.

Dr. Olusola Adeyemi O. is a lecturer in the Department of Geography, University of Ibadan, Ibadan, Nigeria. He is a physical geographer and he specializes in geomorphology, hydrology and terrain analyses. He has been involved in over ten ecological survey about terrain analysis and land use suitability, commissioned by the National Universities Commission in Nigeria. He teaches practical geography, geomorphology, hydrology, geo-morphological techniques, soil and vegetation geography, land evaluation, river basin and aerial photograph interpretation. He provides hands-on teaching experience for his students and demonstrates how process-form interactions operate within drainage basins.

Efosa is an experienced environmental geographer, and he is a lecturer and a researcher/Facilitator with the African Regional Institute for Geo-Spatial information Science and Technology (AFRIGIST) under the auspices of the United Nations Economic Commission for Africa (2011-2017) and the University of the Free State, South Africa (2017-2020). He specializes in the application of machine learning, deep learning, remote sensing, and GIS in ecological modeling, climate change and disaster management in mountainous regions. He has carried out various research, organized workshops, and trained employees of various Government, private and international organizations in the use of remote sensing and GIS applications.

Abstract:

Non-fluvial factors such as climate, lithology and vegetation exercise controls through their direct and indirect impact on fluvial processes and dynamics. Several geomorphologists have highlighted the importance of structure on landform evolution. However, the impact of underlying structure on channel initiation and evolution are still growing.The aim of the workshop is to identify important basin morphometric parameter such as long profiles, slope-area interactions, lineament pattern, river network orientation, hypsometric integral and landform classification using Digital Elevation Model.Long profiles help in the identification of knickpoints and in the determination of concavity of profiles, to unraveling the influence of underlying lithological groupings. The slope-area curves help in discriminating among interacting variables by enhancing classification and identifying processes in operations within drainage basins. Lineament patterns and network orientations are presented on Rose diagrams to show the influence of underlying lithological units on river network orientation and initiation. The Rose diagram also to a large extent present trends that can be compared with network orientations. The shape of the hypsometric curve and integral is much needed for basin evolution. The landform classification is done using machine algorithm in SAGA to enhance rapid classification that can be compared with field observations.

External links:

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Requirements:

Installed software is required for the workshop: ArcGIS ver. 10.X, ArcGIS Pro ver. 10.X, SAGA GIS 7 and Fragstats ver. 4.2. Attendees should mainly use their own laptops for this workshop. SAGA will be available in the lab’s computers. A limited number of workstations equipped with ArcGIS will be available.

Giuseppe Amatulli obtained a B.S and M.S. in Forestry at Bari University (IT, 2000); a M.S. in Geo-Information Sciences at Wageningen University (NL, 2004); a PhD in Forestry at Basilicata University (IT, 2005).He worked at Zaragoza University (2004-2006), JRC of the the European Commission (2006-2012), South Dakota State University (2012-2013). He is currently appointed as Research Scientist in GeoComputation and Spatial Science at Yale University, New Haven, CT, USA.

Abstract:

GDAL and PKTOOLS are powerful commands line utilities mainly used for raster manipulation and analysis.In this workshop we will explain the main principle and ideas behind these tools, by showing simple geo-data processing for raster cropping and reprojection, image masking, spatial and temporal/spectral filtering as well as image classification.We will explain how to maximize computational implementation efficiency and process raster data by building up routines that allow to save temporary rasters outputs in the RAM and use VRT files for tiling operations, in a multicore environment.We expect basic Linux command line knowledge (any language is fine) and a general know-how of geo-spatial data processing.

External links:

During the workshop we will explain the material presented at the URL:

The platform http://www.spatial-ecology.net/dokuwiki/doku.php is an open platform dedicated to the use of different languages for GeoComputation analysis. We encourage users to explore other pages that report examples of language integration and multi-core processing.

Dr. Shen obtained his Ph.D. on quantum chemistry from Carnegie Mellon University. At Yale university he coupled quantum mechanics principles and machine learning algorithms to build the first two-way design diagram that completely mapped out the connections between the chemistry and toxicology spaces. He built spatial models on top of machine learning and large-scale geo-computations to predict the distribution of nitrogen and phosphorus in their varied chemical forms in the streams across the contiguous US. At University of Cambridge he built a model to predict the evolution of influenza viruses. He works with the http://spatial-ecology.net team in various educational and research activities.

Abstract:

The explosive expansion of computer power and data availability has ushered the data science into a new era. Machine learning (ML) based approaches exhibit their unprecedented values in many research domains, including spatial modelling. The workshop is going to be composed of two components: a theoretical lecture on the mathematical foundation of ML and a hands-on tutorial of applying Random Forest (RF), a well established ML algorithm, to model the nitrogen distribution in the streams. In the lecture part, I’ll explain the modelling phiolophy, basic probability theory, non-linearity challenges and ML based approaches. In the case study, I will focus on the ML algorithm with an intention to cover the underlying statistical techniques embedded in RF, including bagging and variable randomisation. Additionally, I will discuss the concept of superparameter space and data transformation. The entire case study journey will begin with the point observations, through model training and validation and will end with generation of maps as the final output.By the end of the workshop, attendees are expected to gain a conceptual grasp of ML, the general procedure to build ML models and how to assess the model performance in spatial modelling.

Dr. Samantha T. Arundel (Sam) earned her Ph.D. in geography at Arizona State University in 2000, and was an assistant and then associate professor at Northern Arizona University until 2009. She then joined the USGS, where she served as elevation specialist for the Applied Research and Technology Branch and led the contour generation development team in contour production for the USTopo product. Then she served as program manager for automation of the National Elevation Dataset. In 2015, Sam moved to the Center of Excellence for Geospatial Information Science, where she is a research geographer conducting terrain mapping and modelling studies. Her research focuses on automating natural feature mapping and modeling using various techniques like traditional raster modeling, GEOBIA and machine learning.

Abstract:

This 2-hour workshop will explain in detail the different USGS elevation products, and discuss the best methods to obtain them, including services and APIs.Discussion will begin with development of the lower resolution seamless products, how they were derived and what areas they cover, and continue to explain the current instance of the highest resolution seamless dataset.We will then discuss the even higher resolution DEMs and lidar point cloud products and derivatives.Finally, users will follow along with exploration of the National Map (TNM) Viewer interface, ftp interface, service access via ArcGIs Pro and use of the TNM API for programmatic access.

The workshop could be extended to a 4-hour workshop with the second half focusing on USGS hydrographic data, if enough participants will subscribe.

Prof. Valente focuses on tectonic geomorphology and active tectonics both at the orogen scale and at the mountain front scale. He is Professor of “Gis e Cartografia geotematica con laboratorio” at University of Naples Federico II, (2020-now). His previous teaching experience is: “Physical Geography”, University of Naples Federico II (2018-2020); “Morphometrical and morphotectonic approach for the reconstruction of vertical motions distribution”, University of Naples Federico II (May 2019); “Morphotectonics and active tectonics: case studies from the southern Apennines (Italy)”, Facultad de Ingenieria, Universidad Nacional de Cuyo, Mendoza (Argentina; November 2018).

Abstract:

A large portion of the Earth’s surface is affected by high seismicity, thus consisting in moderate to high magnitude (M > 6) earthquakes. Seismicity is clustered along active plate margins where the highest topographic peaks of the Earth occur. At the orogen scale, seismicity distribution fits with the distribution of active faulting (both extensional and reverse), that are in many cases placed at the mountain fronts toes, where intensely urbanized areas are often present. Unravelling the spatial distribution of active faulting is so a preliminary and crucial key to decipher seismic risk scenario. The morphometric analysis of drainage networks and mountain fronts may provide useful information in these analyses. In fact, both drainage network and mountain fronts react to a tectonic perturbation by changing their shape (e.g., variation in river pattern and river long profile or concave to convex form of a mountain front scarp). Any variation in both river long profile and mountain front shape may be described by means of morphometrical indexes, so the morphometrical analysis of both features is a very powerful tool to detail active tectonics distribution.